1. Technical Field
The invention relates to methods and apparatus for detecting currency note condition and to currency counting methods and machines, in which a total value of the currency is determined by counting notes of various denominations that may be word, soiled or skewed as they pass through a currency counting machine.
2. Description of Background Art
Many existing currency counting machines determine only the piece count of the currency (i.e., "x" number of bills), leaving it up to the operator to infer the monetary value of the currency being counted. An automated method of determining the denomination of paper currency is a valuable addition to these currency counting machines. With such an automated method, the ease, speed, and accuracy of financial transactions can be increased, thereby increasing the likelihood of detecting both human error and fraud.
United States currency presents unique challenges for denomination recognition by automated methods. Unlike most other currencies, every denomination of currency is printed using the same colors and types of inks, and the physical size of every denomination is likewise identical. As a result, neither the length, width, nor color of a piece of United States currency offers any information regarding that piece's value.
Further challenges arise when attempting to integrate a denomination recognition method into high speed currency counting machines. Typically, the side-to-side position (lateral displacement), orientation (face up or down, top edge leading or trailing), angular skew, and velocity of transport of the notes are poorly controlled.
A light transmissive technique for denomination recognition is disclosed in Kurosawa et al., U.S. Pat. No. 5,542,518. In Kurosawa et al., the image data is processed using a technique involving hyperplanes to separate image data vectors for respective pairs of denominations into two regions. The scanned image data vector is then compared to see which of the two vector regions it is in relative to the hyperplane, and the denomination corresponding to image data in the opposite vector region is discarded. By making several sets of comparisons with image data separated by hyperplanes, the scanned image data is finally identified as being most like one other set of image data for a specific denomination.
The above system limits scanning to specific areas of the note, and thus the above-described recognition system is inherently sensitive to how the note is fed (i.e., the note's lateral position and skew) and note damage.
The above-described recognition system also utilizes hyperplanes (a subset of all correlation techniques) in combination with a binary search technique to determine the category matching the target note. This technique varies from traditional neural networks in which hyperplanes are used in conjunction with other elements to resolve the system in one pass with a higher degree of confidence.